history matching

The authors of this paper propose a hybrid approach that combines physics with data-driven approaches for efficient and accurate forecasting of the performance of unconventional wells under codevelopment.

This paper describes a full-field and near-wellbore poromechanics coupling scheme used to model productivity-index degradation against time.

This paper develops a deep-learning work flow that can predict the changes in carbon dioxide mineralization over time and space in saline aquifers, offering a more-efficient approach compared with traditional physics-based simulations.

This study presents a novel approach to screen thermally stable surfactants at high pressures and high temperatures for the explicit purpose of wettability alteration in the operator’s Eagle Ford acreage.

The authors of this paper present an approach integrating characterization of paleo zones, parameterization of paleo-zone conductivity, and application of flow profiles in a history-matching study of a dual-porosity/dual-permeability model.

The authors of this paper develop a robust history-matched reservoir simulation model capable of predicting polymerflooding performance in the first such pilot to enhance heavy oil recovery on Alaska’s North Slope.

The authors of this paper define a work flow that constrains solutions that match models and field observations and obtains a more-representative model for forecasting and optimizing fracture behavior.

This paper presents an automated calibration process, probabilistic infill well ranking, and location optimization for a major heavy oil field in Colombia with original oil in place of more than 5 million STB.

The authors of this paper present an application of a Bayesian framework for uncertainty assessment and efficient history matching of a Permian CO2 enhanced oil recovery field for reliable production forecast.

The paper describes an approach to history matching and forecasting that does not require a reservoir simulation model, is data driven, and includes a physics model based on material balance.